Discriminative K-Means Laplacian Clustering

Recently, more and more multi-source data are widely used in many real world applications. This kind of data is high dimensional and comes from different resources, which are often the attribute information and similarity information of the same data. It is challenging to use these two types of information to deal with the high dimensional problem simultaneously. A natural way to adopt is a two-step procedure: it utilizes feature integration or kernel integration to combine these two types of information first and then perform dimensional reduction like principal component analysis or various manifold learning algorithms. Different from that, we proposed to deal with these problems in a unified framework which combines discriminative K-means clustering and spectral clustering together. Compared with those separate two-step procedure, information integration and dimension reduction can benefit from each other in our method to promote clustering performance.In addition, discriminative K-means clustering has incorporated K-means and linear discriminant analysis to promote clustering and tackle high dimensional problem. Spectral clustering can reduce the original dimension easily due to the singular value decomposition. Thus it is a good way to combine discriminative K-means and spectral clustering to improve clustering and deal with high dimensional problem. Experimental results on multiple real world data sets verified its effectiveness.